Many applications involve robots that are deployed in a workspace to gather different types of data and upload them to a data center for processing.
For instance, teams of unmanned ground vehicles (UGV) can monitor the temperature, humidity, and stand density in large forests or teams of unmanned aerial vehicles (UAV) can monitor the behavior of animal flocks and growth of the crops in farmlands [1]}.
Due to heterogeneous sensing and motion capabilities, the robots in these applications can gather different types of data in different regions within the workspace.
Thus the robots can be assigned local data-gathering tasks that vary across the team [1]}.
In this work, we employ Linear Temporal Logic (LTL) as the formal language to describe complex high-level tasks beyond the classic point-to-point navigation.
A LTL task formula is usually specified with respect to an abstraction of the robot motion [3]}, [4]}.
Then a high-level discrete plan is found using off-the-shelf model-checking algorithms [5]}, and is executed through low-level continuous controllers [6]}. This framework can be extended to allow for both robot motion and actions in the task specification [7]}.
